1. Can computers think ?
Krishnan
GTC talk
Sept. 18,2011

2. Classic book on the subject

3. Machines vs. Humans
• Machinery outperforms us in physical
ways
– Cars outrun us
– Planes can fly, we cant
• This doesn’t disturb us
• Is thinking a human prerogative ?
– Can mechanical devices out think us

4. What can computers do better ?
• Computations on large numbers
– E.g. Multiplying two 100 digit numbers
• Play chess (and other games)
• Answer natural language questions
– IBM Watson
• House cleaning robots ?
• But does this mean they are intelligent ?

5. Advertisement
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Will you buy ?

6. What is intelligence ?
• Newell and Simon - the use and
manipulation of various symbol systems,
such as those featured in mathematics or
logic
• Large debate in the AI, Psychology,
Philosophy community

7. Alan Turing
• British scientist
– Helped solved the Enigma
Machine (WWII)
– Advances in Probability Theory
• Invented the theory behind
computers
– Turing Machine
– Turing Test

8. The imitation game
• Proposed by Alan Turing in 1950
• It is played with three people, a man (A), a
woman (B), and an interrogator (C) who may be
of either sex. The interrogator stays in a room
apart from the other two.
• The object of the game for the interrogator is to
determine which of the other two is the man and
which is the woman. He knows them by labels X
and Y, and at the end of the game he says either
‘‘X is A and Y is B’’ or ‘’X is B and Y is A.

9. Turing test: Distinguish man and
machine
What would it take for a computer’s thoughts to be indistinguishable
from a human’s?

11. Chinese Room
• The system comprises:
– a human, who only understands English
– a rule book, written in English
– two stacks of paper.
• One stack of paper is blank.
• The other has indecipherable symbols on them.
• In computing terms
– the human is the CPU
– the rule book is the program
– the two stacks of paper are storage devices.
• The system is housed in a room that is totally sealed with the
exception of a small opening.

12. Chinese Room: Process
• The human sits inside the room
waiting for pieces of paper to be
pushed through the opening.
• The pieces of paper have
indecipherable symbols written
upon them.
• The human has the task of
matching the symbols from the
"outside" with the rule book.
• Once the symbol has been found
the instructions in the rule book
are followed.
– may involve writing new symbols
on blank pieces of paper,
– or looking up symbols in the stack
of supplied symbols.
• Eventually, the human will write
some symbols onto one of the
blank pieces of paper and pass
these out through the opening.

13. Chinese Room: Summary
• Simple Rule processing system but in
which the “rule processor” happens to be
intelligent but has no understanding of the
rules
• The set of rules might be very large
• But this is philosophy and so ignore the
practical issues

14. Searle’s Claim
• We have a system that is capable of passing the
Turing Test and is therefore intelligent according
to Turing.
• But the system does not understand Chinese as
it just comprises a rule book and stacks of paper
which do not understand Chinese.
• Therefore, running the right program does not
necessarily generate understanding.

15. Strong AI
• Strong AI is artificial intelligence that matches or
exceeds human intelligence
• The intelligence of a machine can successfully
perform any intellectual task that a human being
can
• Advocates of "Strong AI" believe that computers
are capable of true intelligence
• They argue that what intelligence is strictly
algorithmic, i.e., a program running in a
complex, but predictable, system of electro-
chemical components (neurons).

16. Strong AI
• Many supporters of strong AI believe that the
computer and the brain have equivalent
computing power
• With sufficient technology, it will someday be
possible to create machines that have the same
type of capabilities as humans
• However, Strong AI's reduction of
consciousness into an algorithm is difficult for
many to accept
• Proponents are: Ray Kurzweil, Marvin Minsky
etc.

17. Weak AI
• The Weak AI thesis claims that machines, even
if they appear intelligent, can only simulate
intelligence
• They will never actually be aware of what they
are doing
• Some weak AI proponents believe that human
intelligence results from a superior computing
mechanism which, while exercised in the brain,
will never be present in a Turing-equivalent
computer
• Roger Penrose is a proponent of Weak AI

18. What can a computer compute ?
• Hardware – circuits, gates, wires
• Software – Program that runs on the
hardware
• Turings remarkable discovery – All
computing machines are equivalent in
what they can do
– Though speeds may differ
• All computers are equivalent to a
Universal Turing machine

19. Algorithms
• The word comes from the Persian
mathematician Abu Jafar Mohammed ibn
Musa al Khowarizm
• He wrote a book
– Kitab Al-jabr wal-muqabala
• Example algorithm
– Euclids algorithm for highest common factor
of two numbers

20. Euclid’s algorithm
This is a systematic procedure that will work for any two
positive integers

21. Hilbert's programme:
•To establish the foundations of
mathematics, in particular by clarifying
and justifying use of the infinite:
``The definitive clarification of the
nature of the infinite has become
necessary, not merely for the special
interests of the individual sciences but
David Hilbert for the honour of human understanding
(1862-1943)
itself.''
•Aimed to reconstitute infinitistic
mathematics in terms of a formal
system which could be proved
(finitistically) consistent, complete
and decidable.

22. •Consistent: It should be impossible to derive a contradiction
(such as 1=2).
•Complete: All true statements should be provable.
•Decidable: There should be a (definite, finitary, terminating)
procedure for deciding whether or not an arbitrary statement is
provable. (The Entscheidungsproblem)
There is the problem. Seek its solution. You can find
it by pure reason, for in mathematics there is no
ignorabimus.
Wir müssen wissen, wir werden wissen

23. Bertrand Russell Alfred Whitehead
(1872-1970) (1861-1947)
•Russell's paradox showed inconsistency of naive foundations such
as Frege's: {X | X∉X}
•"The set of sets which are not members of themselves"
•Theory of Types and Principia Mathematica (1910,1912,1913)

24. Kurt Gödel
(1906-1978)
•Uber formal unentscheidbare Sätze der Principia
Mathematica und verwandter Systeme (1931)
•Any sufficiently strong, consistent formal system must be
•Incomplete
•Unable to prove its own consistency

25. Alan Turing
(1912-1954)
•On computable numbers with an application to the
Entscheidungsproblem (1936)
• Church, Kleene, Post

27. Turing machine
• Imagine a device for carrying out a
computational procedure (like Euclid’s algorithm)
• What is the general form such a machine can
take ?
– Machine should have discrete states (large but finite
in number)
– Input/Output of unrestricted size
– Finite number of states implies cannot internalize the
data

28. A Turing Machine
Tape
...... ......
Control Unit Read-Write head
Adapted from slide by Costas Busch, http://www.cs.rpi.edu/
28

29. The Tape
No boundaries -- infinite length
...... ......
Read-Write head
The head moves Left or Right
Adapted from slide by Costas Busch, http://www.cs.rpi.edu/
29

30. ...... ......
Read-Write head
The head at each time step:
1. Reads a symbol
2. Writes a symbol
3. Moves Left or Right
Adapted from slide by Costas Busch, http://www.cs.rpi.edu/
30

31. Example:
Time 0
...... a b a c ......
Time 1
...... a b k c ......
1. Reads a
2. Writes k
3. Moves Left
Adapted from slide by Costas Busch, http://www.cs.rpi.edu/
31

32. The Input String
Input string Blank symbol
...... ◊ ◊ a b a c ◊ ◊ ◊ ......
head
Head starts at the leftmost position
of the input string
Adapted from slide by Costas Busch, http://www.cs.rpi.edu/
32

33. States & Transitions
Read Write
Move Left
q1 a → b, L q2
Move Right
q1 a → b, R q2
Adapted from slide by Costas Busch, http://www.cs.rpi.edu/
33

34. John von Neumann architecture

35. Ways that the brain differs from
a conventional computer:
• Very few cycles available to make decisions
• Massively parallel: 100 trillion interneuronal
connections
• Combines digital & analog phenomena at
every level
– Nonlinear dynamics can be modeled using digital
computation to any desired degree of accuracy
– Benefits of modeling using transistors in their analog
native mode
35

36. Ways that the brain differs from
a conventional computer:
• The brain is self-organizing at every level
• Great deal of stochastic (random within
controlled constraints) process in every aspect
– Self-organizing, stochastic techniques are routinely
used in pattern recognition
• Information storage is holographic in its
properties
36

37. level of complexity we can
manage
• Only about 20 megabytes of compressed design
information about the brain in the genome
– A brain has ~ billion times more information than the genome
that describes its design
• The brain’s design is a probabilistic fractal
• We’ve already created simulations of ~ 20 regions (out
of several hundred) of the brain
37

38. Is the brain a computer ?
• If it is
– then the limits of
computers are the
limits of the brain
– A brain cannot do
what a computer
cannot
• Counter example: The
halting problem

39. What computers cannot do: The
halting problem
• An example of something that is not computable.
• Created by Turing in 1936 to define a problem which no
algorithmic procedure can solve.
• Can we write a program that will take in a user's program
and inputs and decide whether
– it will eventually stop, or
– it will run infinitely in some infinite loop ?
• The answer is No; there is no procedural method for
answering the halting problem
• Human beings can do this by inspection
– Does this imply human brain can perform non-computational
procedures ?
– If so what is the mechanism ?

40. Penrose’s belief
• There is some part of conscious thinking
that cannot be simulated on a computer
• What is it in the physics of the world that
cannot be controlled computationally ?
• Is quantum mechanics the answer ?
– In quantum mechanics, a particle can be in
two places at the same time
– Quantum wave function collapse happens
when the particle is observed

41. How are new ideas formed
• Physical brain activity—rapid trials of
combinations of growing and contracting
dendritic spines, which stretch out to the
synapses that separate a nerve cell from
its neighbor
• These take place below one graviton
(particles that transmit gravity)

42. How are new ideas formed ?
• How is a final dendrite construction settled
on when our mind grasps a concept or
glimpses a new symphonic work?
• Microtubules in brain orchestrate collapse
of the quantum wave function

43. Further readings
• http://www.brainpickings.org/index.php/20
10/09/07/what-does-it-mean-to-be-human/